文章基本信息

标题：KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation
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作者：Michele Borassi ; Emanuele Natale
期刊名称：LIPIcs : Leibniz International Proceedings in Informatics
电子版ISSN：1868-8969
出版年度：2016
卷号：57
页码：20:1-20:18
DOI：10.4230/LIPIcs.ESA.2016.20
出版社：Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
摘要：We present KADABRA, a new algorithm to approximate betweenness centrality in directed and undirected graphs, which significantly outperforms all previous approaches on real-world complex networks. The efficiency of the new algorithm relies on two new theoretical contributions, of independent interest. The first contribution focuses on sampling shortest paths, a subroutine used by most algorithms that approximate betweenness centrality. We show that, on realistic random graph models, we can perform this task in time |E|^{1/2+o(1)} with high probability, obtaining a significant speedup with respect to the Theta(|E|) worst-case performance. We experimentally show that this new technique achieves similar speedups on real-world complex networks, as well. The second contribution is a new rigorous application of the adaptive sampling technique. This approach decreases the total number of shortest paths that need to be sampled to compute all betweenness centralities with a given absolute error, and it also handles more general problems, such as computing the k most central nodes. Furthermore, our analysis is general, and it might be extended to other settings, as well.
关键词：Betweenness centrality; shortest path algorithm; graph mining; sampling; network analysis